We started the digital transformation (DT) discussion at a strategic level, where we talk about how DT must transform more than just the technology, but also the people process and culture of the entire company. Then we shifted to a more operational level and talk about why business must adopt Geoffrey Moore’s 4 gears model in order to survive the digital revolution. From this perspective, DT is simply the roadmap of the journey that transforms the business from a traditional 2-gears model to the new 4-gears model.
Over the last 2 posts, we derived some practical tips from behavior economics that help practitioners overcome their company’s resistance to change.
The starting point of DT should be something your company is already doing—acquisition (i.e. marketing).
The ramp from acquisition to engagement, and then to enlistment will emerge naturally as a necesity for the continual success of your DT program.
Since this baby-steps approach is driven by success, it’s harder for enterprises to abandon the DT initiative during this multi-year journey, because they would be forgoing success. Although this provides an operation guideline for spinning the 4 gears, the specific of how you transform your marketing and what precisely you do to engage and enlist is dependent on your vision of DT. Moreover, the marketing technology landscape is huge and complex. And there are many ways you can transform your digital marketing operation.
Today we will go back to the strategic level at the beginning, where we identified 3 ingredients that successful DT must have from the start. We have discussed many concepts and models around DT. The following picture is a good visual summary of what we’ve discussed so far.
The Challenge of Connecting the Dots
Recall from the very first vlog of this mini-series, we found 3 initial ingredients that are crucial to the long-term success of your DT project.
A customer-centric strategy
A clear vision of what DT means to your brand
The right technology that is fully integrated into your digital ecosystem and simple to use
Although one can easily argue the importance of any one over the other two, they are all equally important. We must have all 3 of them, or we should wait until we have them all before we embark on the DT journey. In reality, the 3 success ingredients all influence each other. Creating a customer-centric DT strategy is always an iterative process, as is forging a vision and choosing the right technology.
The challenge is that we now know the end state, and we have a way to get there with minimum corporate resistance (i.e. baby steps). This will further constrain the vision and the customer-centric strategy we can explore. Not all vision and strategy will bring us to the 4-gears end state. Of those that will get us there, not all will work with the baby steps approach we outlined.
For example, your vision may be to transform your email marketing by using big data to better segment and target your audience. This can even be done in a customer-centric fashion to give customers a better customer experience (CX) by serving them hyper-relevant email that they want rather than spamming them. And you have chosen the best technology in the market that is simple to use. Although this vision encompasses all 3 success ingredients, the success of this DT initiative will not lead your company to spin the engagement gear. Because successful email marketing improves click through rate, which drives more traffic to your e-commerce sites and ultimately increases monetization. Evidently, we are back to the 2-gears model again. Albeit profitable in short-term, this won’t sustain and won’t save your business from being outcompeted by those who’ve learned to spin the 4 gears.
Similarly, revamping your web analytics, SEO, webinar, or listening platform, probably won’t help your company spin the 4 gears either. Forging a vision or a customer-centric strategy under the constraints imposed by the 4-gears model and the baby steps principle is very hard, because we must connect all the dots in prospect. But without them, we risk either miss the boat at the end or suffer corporate resistant along the way, none of which we want.
What is a Total Community?
In our experience, it’s often best for you to come up with a vision that’s compatible with your brand’s mission, because that should be unique to your company. But there is a customer-centric strategy that is consistent with the 4-gears model and connects all the dots along the way. It’s known as the total community strategy, and it involves engaging the true community of your brand on digital channels. The only disclaimer I like to point out is that this is not the only strategy that connects all the dots. However, it is one that I know is repeatable, because it’s been tested over many years of Lithium’s business operation.
To understand total community, we have to understand the distinction between social networks and communities. Most social media practitioners probably do know the difference between these 2 social structures and treat them equivalently—they are all just social media. While this isn’t wrong, it misses some important properties that have limited many practitioners from realizing the full potential of their social media initiatives. In short,
A community is a group of people held together by some common interest.
A social network is a group of people held together by interpersonal relationships.
For a more detail exposition on this topic, please review my cyber-anthropology mini-series. If you really want to dive deep and understand the communication, structural, and mathematical distinction between communities and social network, I recommend following this academic discussion.
Given this basic understanding of community, what is the true community of your brand? Most people probably think it’s your brand’s online community. This is not quite correct. Although the online community is certainly part of the true community of your brand, it is only a very small part of it.
To answer this question more accurate, we have to use our definition of a community and ask, “Who are all the people who have a common interest around your brand?” The group of all people who have a common interest in your brand is the true community of your brand, regardless of where they may be. Your brand’s true community is defined by the shared interest about your brand, not the geographical or digital boundaries. This group certainly includes customers and prospects, but it also includes employees, partners, and influencers etc. To avoid confusion with the usual connotation of the word “community,” and to emphasize the breadth and completeness of your brand’s true community, we define to this group as the “total community”
So, by definition, your brand’s total community is the true community of your brand.
Total Community as a Customer Centric Strategy
The concept of total community can be quite confusing, because it can mean several different things depending on your vantage point. There are at least 3 perspectives that I am aware of.
From a social science perspective, the total community of your brand is, by definition, the group of people who have a common interest in your brand. So it’s just a group of people. I must ephasize that this group doesn’t include Your competitors surely have an interest in your brand, but it’s usually not a “common” interest. They are more interested in your demise than your success.
From a business persp ective, total community is also a The total community strategy says brands must engage their total community (the group of people that constitute their true community) to provide them with an awesome CX wherever they may be. Since your total community consists of a very diverse group, they are dispersed across many digital channels. Some of your customers are certainly in your online community, but the majority of your customers are not. Yet, they are participants in various social networks. Your employees are inside your enterprise, and your partners and influencers may be on several 3rd party websites. The total community strategy says you must engage and provide excellent CX in all these digital channels (i.e. online customer communities, the greater social web, inside your enterprise, as well as 3rd party websites.). Like it or not, your total community is digitally distributed.
Finally, from a technology perspective, total community is also a technology platform that enables brands to engage their total community and give them great CX wherever they may be. So the total community platform is a software platform that helps brands provide exceptional and personalized CX while engaging their online customer communities, the greater social web, inside your company, as well as on 3rd party websites.
Conclusion
Now we have 3 different vantage point to help us understand DT.
The 4-gears model provides an image of how your business should operate near the end of your DT journey. It serves as a beacon to give you directions along the lengthy DT journey so you will never lose sight of your final goal.
DT is a roadmap that guides the business transformation process. It starts with the adoption of new digital technology, but need to manage the change of processes, employee behaviors, and the culture for the entire company. Since this is a multi-year journey, we’ve use behavior economics to help us overcome corporate inertia along the journey.
Finally, the total community strategy is the customer-centric strategy that helps shape your vision and guides the technology selection at the beginning of your DT initiative. This will ensure that you have all 3 success ingredients before you start the transformation process.
We came a long way. In fact, we have traversed the DT journey from start to finish, and then back to the beginning again. Below are all the prior entries of this mini-series. Next time let’s try to wrap up this DT mini-series.
Successful Digital Transformation Must Go Beyond Digital to the People, Process, and Culture
Why Transform Your Business Digitally? — A History Lesson through the 4 Gears
The Offensive Logic to Digital Transformation — Customer Experience
The Next Level of Digital Engagement
The Right Start — The Behavior Economics of Successful Digital Transformation: Part 1
The Ramp Up — The Behavior Economics of Successful Digital Transformation: Part 2
*Image Credit: ar130405.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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Welcome back to the 6th entry of this digital transformation (DT) mini-series. Over the course of this mini-series, we’ve learned a powerful framework (i.e. Geoffrey Moore’s 4-gears model) that help businesses survive and thrive in today’s digital economy. There is no doubt that the 4-gears model is related to DT, since both are trying to help companies survive the digital disruption, but their relationship isn’t obvious. I will fix this today before diving deeper into the behavior economics of successful DT.
Since this post builds on the previous discussions of this mini-series, if you missed any of the earlier entries, it’s a good idea to review them before moving on:
Successful Digital Transformation Must Go Beyond Digital to the People, Process, and Culture
Why Transform Your Business Digitally? — A History Lesson through the 4 Gears
The Offensive Logic to Digital Transformation — Customer Experience
Gear Up for theNext Level of Digital Engagement
The Right Start — The Behavior Economics of Successful Digital Transformation: Part 1
Digital Transformation vs. the 4-Gears Model
The 4-gears model is a sustainable business model in the digital age. It identifies what businesses must do differently to stay competitive in the digital marketplace (i.e. they must learn to engage and enlist in addition to acquire and monetize). But it doesn’t show us how to get there or even where to begin. In fact, since the 4-gears model is a loop, you can begin anywhere, as long as you can get all 4 gears to spin in sync eventually.
On the other hand, DT can be view as the programmatic execution of the required changes for companies to learn to spin the 2 new gears, and eventually get all 4 gears in sync. Think of the 4-gears model as a snapshot of the final operating state of a thriving enterprise in the digital age. Then DT is a roadmap that shows us how to get to that final state. It guides the implementation of all the necessary technologies, processes, and programs to transforms the company from its current state (2-gears) to the final state (4-gears).
As such, DT is often a multi-year journey. It invariably starts with the adoption of some digital technologies to transforms one part of your business operation. But it must eventually change the people, process, and the culture throughout your entire company in order to last. This is very difficult, because many things could change over the course of such a long journey—stakeholders may leave, funding may be cut, and the technology could change dramatically. When such disruptions occur along the DT journey, corporate inertia will often revert the company back to business as usual (2-gears), which will eventually kill the business.
How can we mitigate the detrimental impacts of these external factors that we don’t control?
Overcoming Corporate Inertia with Baby Steps
The solution is to approach this massive transformation in a more agile fashion, with smaller increments and more iterations toward the final outcome. This is also known as the principle of baby steps in behavior economics and gamification, which can effectively drive the organizational learning and behavior changes introduced by DT. This principle has 2 phases:
Pick the right starting point: You must start any change with something that people are familiar with and are already doing (i.e. acquisition or monetization). This will achieve the lowest barrier of entry, and therefore maximize the initial adoption rate.
Build a ramp to the final goal: You must break up any big change into smaller intermediary steps to create a ramp. This will make it easier for people to advance towards the final behavior outcome (i.e. getting all 4 gears to spin in sync), and lower the abandon rate during the process.
By applying baby steps to DT, we are essentially driving the largest possible population as far along the transformation process as possible. We’ve already discussed phase 1 in the previous post of this mini-series. Although we can, in theory, begin the DT journey with the monetization gear (e.g. with mobile payment, IoT, etc.), it’s much easier to start with the acquisition gear (see the previous post for explanation).
Today, we will address phase 2. Because the precise ramp is dependent on your vision, we will use the example of a social-driven DT to illustrate the ramp from acquisition to enlistment.
Building the Ramp from Acquisition to Enlistment
Start with social acquisition (marketing): Publish hyper-relevant content on social channels (i.e. the right content, on the right channel, at the right time). Since this is where we will begin our DT journey, it will need to have the 3 success ingredients from the start:
Customer centricity: It must improve the customer experience (CX) for your customers.
A clear vision of what DT means to your business: It must improve your marketing performance as measured by standard marketing KPIs.
The right technology: It must improve their productivity of your marketing team and simplify their work through integration.
Since this is something that brands already do and understand, there is no need to create any urgency or compelling event to sell it. The improved marketing performance and employee productivity should suffice to drive the initial adoption.
Due to the hyper-relevant nature of these content, consumers will naturally engage them more frequently. This immediately puts the ball back in the brand’s court. The need to respond to customer reactions, comments, or inquiries will compel brands to start engaging with their customers. The better your social marketing perform, the more conversation there will be for you to respond, which creates a greater need to engage. So this is a natural ramp to spin the next gear—engagement.
Once brands start to engage on social media, they will quickly discover the immense scale of social. They will realize that no matter how much they try, they just can’t respond to all the conversations on the social web. The desire to scale their engagement will naturally compel brands to enlist customers for help. Again, the more you engage and the deeper you engage, the more you will need to scale, which creates a greater need to enlist. This is another natural ramp to spin the next gear—enlistment.
Evidently, going from acquisition to engagement, and then to enlistment is a course of natural progression. The success of each step along the way creates the urgency and need for the next. During this progression, brands will gain sophistication with social interactions and be more comfortable with the lack of control. They will also recognize the long-term value of engaging and enlisting customers and be more comfortable with the delayed ROI. Meanwhile, this also gives brands the time to learn the best practice to manage their social customers and implement the internal processes to help them realize the full potential of enlistment. For example, tribal knowledge creation, crowdsourced ideation and prioritization on product improvements, and co-creation of new product and services.
By building this ramp from acquisition to enlistment, many of the common objections for diving in with engagement and enlistment can be resolved along the way as you mature. Of course, the right technology must also be in place to facilitate the spinning of the 2 new gears (i.e. engage and enlist).
Conclusion
Today, we accomplished 2 things. First, we clarified the relationship between the 4-gears model and DT. The 4-gears model provides an image of what a thriving and sustainable digital business will look like operationally. In contrast, DT provides the roadmap that helps organizations get there by implementing the necessary changes in technologies, processes, programs, etc.
Second, we’ve demonstrated the power of baby steps in overcoming corporate inertia. So if we use baby steps to (1) choose the right starting point and (2) build a ramp to the final outcome, we can use it to drive the organizational changes introduced by DT effectively. Specifically, we can leverage baby steps to drive the shift from the traditional 2-gears model to the future-proof 4-gears model.
What does this mean to you?
If you are hitting walls trying to get your business to engage and enlist, it’s not your fault. Your organization and leadership are just conservative. The solution from behavior economics is to not start with engagement or enlistment, even though that’s where you want your company to go. Instead, start with acquisition—marketing (which your business is already doing) and demonstrate the superior result of your digitally transformed social marketing. Then just look ahead and plan along, because the rest of the DT journey will gently reveal itself due to the ramp we created from the baby steps principle.
*Image Credit: Comfreak, 3dman_eu, and PublicCo.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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Let’s continue the discussion of digital transformation (DT), but we will take a different perspective today. As some of you might know, I’ve always had a keen interest in human behavior. I’ve published many articles on gamification, behavior economics, and its application in the business world. Since DT is typically a multi-year project with many stakeholders and complex human factors, perhaps we can apply a little behavior economics (or even gamification) to this transformation process.
However, DT is also a journey that starts with the adoption of some technology to transform some part of your business, where should you start? We’ve already learned that a customer-centric DT strategy is crucial to paving the road to success, but there are still many parts of a business that could interact with customers. How should you phase this multi-year project? Where should it end up? These are the questions we will explore over the next few posts. Today we will focus on choosing the right starting point.
If you missed the previous blog posts on this topic, I recommend reviewing some of the background materials before moving forward. The related blog posts can be accessed here:
Successful Digital Transformation Must Go Beyond Digital to the People, Process, and Culture
Why Transform Your Business Digitally? — A History Lesson through the 4 Gears
The Offensive Logic to Digital Transformation — Customer Experience
Gear Up for the Next Level of Digital Engagement
Engagement is Hard, Enlistment is Even Harder
From the 4-gears model, we know that business must learn to spin the engagement and enlistment gears in order to survive the digital disruption. So it’s natural for practitioners to start spinning these 2 gears right from the beginning. There is nothing wrong with this. Many digital visionaries have already gone down this path, and they are enjoying the fame and glory from their success. But it really takes a visionary who have a strong conviction (which is rare) to pull this through. However, most business people are pragmatists and conservatives. How should the majority of the businesses (~68%) tackle DT?
The answer is simple: don’t start with engagement or enlistment, because these 2 gears are relatively new compared to acquisition and monetization. Most enterprises have a natural tendency to resist jumping into something new that they don’t fully understand. Although many brands (even the conservative ones) understand the need to engage, many practitioners are still being held back. Here are some of the most common reasons for their resistance.
Lack of urgency: it’s a nice-to-have, not a must-have, so it can wait
Lack of resources: both funding and human resources
Lack of industry best practice (although this is changing as we speak): companies must learn to engage and manage customers (in the case of enlistment) who probably don’t want to have a relationship with the brand
Lack of standard ROI models for different engagement and enlistment use cases (this is also changing)
Incomplete measure of engagement: missing the depth dimension of engagement
In addition to the common objections for diving in with engagement, enlistment often faces a few extra challenges of its own.
Unfamiliarity: most enterprises probably never even heard of enlistment, but related concepts, like crowdsourcing, is gaining popularity in business
Lack of business processes for enlistment: successful enlistment requires brands to incorporate customers’ voices into their company’s routine operation, but many companies don’t have any of these processes in place
Lack of control: enlistment require the business to collaborate and rely on customers, but the company has no control over what their customers will do or say
Long time-to-ROI: even in cases where a clear positive ROI is demonstrable, it is only realizable much later, because it takes a long time to enlist customers
To overcome all these challenges within a traditional company is truly a herculean effort, and that is why those who did it deserve the fame and glory of heroes.
Where Should You Begin Your Digital Transformation Journey?
If your DT efforts were dismissed, perhaps your organization isn’t ready for prime-time engagement and enlistment yet. That doesn’t mean you should just sit and wait, because the price of doing nothing may be the end of your business. So this is where you need to apply a little behavior design or gamification to drive the adoption behavior of your organization.
One of the most powerful behavior-driving principles for large groups of people with disparate motivation is the concept of baby steps. In fact, this principle is so crucial that it’s one of the core tenets of successful gamification. The idea is simple and has 2 phases. First, if you want an organization to adopt something new and unfamiliar, it’s best to start from something that they already know and are already doing. The next phase involves building a ramp for your company to advance towards the final behavior outcome, and we will cover this in the next post.
If the final behavior is for your company to learn to engage and enlist your customers (which they haven’t started), then you should not start with these 2 gears. The fact that they have not started engaging or enlisting by now means that these 2 gears are probably too challenging for your organization. You must start with one of the gears they are already spinning—acquisition (marketing) or monetization (transaction).
In theory, it doesn’t matter which gear you choose to begin your DT journey, since the 4 gears is a loop anyway. In practice, however, it’s a lot easier to start with acquisition for several reasons:
Most of the enterprises (even the pragmatists and conservatives) are either already doing some forms of digital acquisition.
It’s much easier to create a customer-centric strategy around digital acquisition, and customer centricity is crucial to the long-term success of your DT initiative.
There are also a lot more technologies in the market that can help you digitally transform your acquisition gear than do the monetization gear. If you don't believe me, just take a look at this marketing technology landscape infographic.
Digitally Transforming Your Acquisition Gear
Now that we have chosen to begin your DT journey with acquisition (i.e. marketing), how should you transform this business function digitally? I wish I could to tell you, but unfortunately, this is unique for every business. More specifically, it depends on your vision of what DT really means to your brand. And this can be very different for every organization.
Now, you might recall from the first vlog entry of this mini-series where we discuss the 3 most important failure modes that must be addressed at the beginning of every DT project. Addressing these failure modes has led to the 3 initial ingredients that are crucial to the long-term success of DT projects:
A customer-centric strategy
A clear vision of what DT means to your business
The right technology that is fully integrated into your digital ecosystem and simple to use
Since we are beginning the DT journey with acquisition, we must make sure these 3 ingredients are present from the get-go. This can often be established with the 3 sets of questions below:
Do you have a customer-centric acquisition strategy? Are you improving the customer experience (CX) when acquiring their attention on digital channels? Since a good CX during acquisition often means more relevant and personalized messaging, are your marketing content personalized? Are they hyper-personalized?
Is your DT helping your business acquire more effectively and more efficiently? Since acquisition (marketing) is something that every company already does, there is an established standard to measure its performance. So are you improving the performance of your existing marketing efforts? Can you measure this improvement by standard marketing KPIs?
Can the new technology be fully integrated with the rest of your marketing technologies? Does the technology simplify your marketing team’s work? Can your marketing team use the technology without much training? Does the technology increase the efficiency and productivity of your marketing team even factoring in the learning curve?
If the answer is “YES” for all the questions above, then the DT of your marketing operation is on the right track.
Conclusion Starting your DT journey by engaging and enlisting customer is often difficult (though not impossible) for most of the traditional enterprises. Because these 2 gears are relatively new compared to the acquisition and monetization gears, many DT practitioners still get pushed back when starting with the engagement or enlistment gears. We can apply the principle of baby steps to increase the success rate of this challenging transformation process. This is accomplished by starting with the acquisition gear (i.e. marketing), because most companies are already familiar with marketing and are already doing it.
How you transform your marketing with digital technology is specific to your business, but it must contain the following 3 success ingredients:
Improving the CX for your customers
Improving the performance of your company’s marketing KPIs
Improving the productivity of your marketing teams
Picking the right place to start is an important first step. Next time we’ll build the ramp that enables your company to take baby steps toward the final behavior outcome—thriving in today’s digital economy. And that will require all 4 gears (i.e. acquire, engage, monetize, enlist) to spin in sync.
*Image Credit: tpsdave, geralt, and HypnoArt.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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Over the past 2 entries of this digital transformation (DT) series, we’ve gained a deeper understanding for both the defensive and offensive reasons why DT is an inevitable course for modern enterprises. We’ve also introduced the 4 gears model to help us understand why that is the case. Today, we will dive deeper to understand the 2 new gears (i.e. engage and enlist) of the 4 gears model.
In case you’ve missed the previous entries or just want a refresher, they are all accessible here:
Successful Digital Transformation Must Go Beyond Digital to the People, Process, and Culture
Why Transform Your Business Digitally? — A History Lesson through the 4 Gears
The Offensive Logic to Digital Transformation — Customer Experience
Most modern enterprises are already familiar with acquisition and monetization, because they had over a century of practice spinning these 2 gears. Engagement and enlistment are relatively new to the business world, especially enlistment, but brands must learn to spin these 2 gears to survive. So how should brands view these 2 new gears?
A Deeper Look at Customer Engagement
Today, the concept of customer engagement is not foreign to brands, and most marketers understand that it’s simply any interaction between the customers and the brand. However, many brands fail to understand one crucial point about engagement; and that is, it has 2 dimensions: both breadth (how many) and depth (how deep).
This is very different from acquisition, where the only dimension of concern is breadth—how many people’s attention you’ve acquired (recall that the acquisition gear is merely acquiring attentions). Due to people’s familiarity with acquisition, many practitioners today are still quantifying engagement using only the breadth measure (i.e. some variants of how many you’ve engaged). Although this is not completely wrong, it’s missing half of the story. Moreover, the depth of engagement is arguably even more important than the breadth of engagement. Let’s try to understand why.
The 4 gears model clearly shows the engagement gear feeding into the monetization gear. This means the ultimate purpose of engagement should be to help you sustain monetization by capturing the consumer’s attention longer. In practice, the most important outcome of engaging your customers is that you will build stronger relationships with them. The stronger customer relationships imply that these customers will be more loyal, which manifests in more repeat business with you, and hence help you sustain monetization.
Now, given that both the breadth and the depth of engagement are important, do you think it’s the breadth or depth that is going to help you build stronger relationships with your customers?
The answer should be clear. Engaging millions with no depth is useless, because it’s not going to help you sustain monetization. But the contrary is also true: engaging deeply with only a few is probably not going to impact your monetization significantly either. Brands must learn to balance these 2 dimensions of engagement to optimize its long-term impact on monetization. More importantly, brands must gain sophistication with spinning the engagement gear, and not just blindly follow other’s engagement tactic. Engagement that doesn’t end up building stronger relationships with your customer and lead to more loyal customers is a waste of resources.
A Very Brief History of Engagement
Today, quite a few digitally savvy brands are starting to get a hang of social engagements. But this was not the case about 7 years ago (i.e. around 2010). Back then, most brands still didn’t understand why they need to engage their customers. Many are still operating under the traditional 2 gears model (i.e. focused only on the acquisition and monetization gears). But today (7 years later), every brand I talk to knows they need to engage.
This doesn’t mean brands have perfected the art of engagement yet. Clearly, brands can’t master engagement when they don’t even measure it accurately, due to the lack of a metric for engagement depth. Brands still have many questions about engagement. For example, who should you engage? Do you engage influencers or all customer? How do you prioritize them? How to engage them most effectively? When and where should you engage, which channels? And what’s the ROI of engagement?
Despite all the unknowns and uncertainties around customer engagement, one thing that every company agrees on is the fact that they need to do it. It's well known that innovation adoption is not uniform. The innovators and visionaries will lead the pack, but they are typically a small fraction of the population (~16%). The bulk of the population are pragmatists (~34%, a.k.a. the early majority) and conservatives (~34%, a.k.a. the late majority) who will catch up slowly.
It took ~7 years for even the conservatives (i.e. the late majority) to recognize the importance of customer engagement, and it will probably take a few more years before brands master it. Although this sounds like a long time, it’s relatively short compared to the business transformation created by the transportation and communication revolution (where companies switch from a 1-gear to a 2-gears operation).
Now, if you are the innovators and visionaries, you probably already engage your customers. So the natural question is, “what comes next?”
From Engagement to Enlistment
The next gear that brands must learn to spin is enlistment, which is to leverage your customer to help you do work that’s normally done within your enterprise. Unlike engagement, customer enlistment is still a foreign concept to many brands. Enlistment today is like engagement ~7 years ago. Many brands don’t know what is it, or why they need to enlist customer when they have employees. However, this gear is crucial because it closes the feedback loop, and it’s what makes this model scalable and sustainable in the digital age.
Despite its importance, customers are not obliged to help any brand in any way, and they are not going help you just because you want to enlist them. So how do you spin the enlistment gear?
To answer this riddle, we need to understand the relationship between engagement and enlistment. The way to think about these 2 new gears is that they are simply the 2 extremes of a continuous engagement spectrum. Since engagement is any interaction between the consumer and the brand, it covers everything consumers do that touches the brand. Whether it’s visiting the brand’s store, watching an ad about their new product, liking/sharing a video they published, or getting help from their support agent, all are valid forms of engagement.
The key is recognized that every engagement with a brand has a different depth. So engagement is really a whole spectrum of interactions ranging from the shallowest (passive engagement) to the deepest (active engagement). The depth of engagement correlates with the amount of consumer resource required (e.g. time, effort, etc.). So the engagement spectrum starts with activities that require little effort: consume, share, curate, to create, and finally to co-create, which could require a lot of time, effort, and even mental resources.
When customers are co-creating with you, they will be collaborating with your product or design teams to help create a better product. Since customers are not obliged to collaborate with you or help you do anything, when they are co-creating with you, they are definitely enlisted. But enlistment starts much earlier on the engagement spectrum. When customers are sharing your content with their friends (which they are also not obligated), they are helping you market your product, which is normally done by your marketing team. So sharing can be viewed as a light form of enlistment.
Evidently, engage and enlist are just the 2 ends of a continuous spectrum. Enlistment is just the deepest form of engagement. And engagement is just a very shallow form of enlistment, with possibly the exception of “consume,” because it’s unclear how consumption of brand content helps anyone in your company do his/her work. So, if you are one of those savvy brands who is already engaging customers digitally, you are on the right path to customer enlistment. Just engage your customer deeper. Before long, your customers will start helping you out, if you make helping easy and rewarding.
Conclusion
To survive the digital revolution, brands must adopt a new strategic model that involves spinning all 4-gears (i.e. Geoffrey Moore’s 4 gears model: acquire, engage, monetize, enlist). Companies must learn to spin the engagement and enlistment gears in addition to the familiar acquisition (marketing) and monetization (transaction) gears. Although many brands are already engaging their customers, few have the process and analytics to get to the goal of engagement, which is to help you sustain monetization by building stronger relationships with your customers.
On the contrary, enlistment (leveraging customers to help out different aspects of your business) is still a foreign concept. To start spinning the enlistment gear, companies need to start paying more attention to an important but overlooked dimension of engagement—its depth. This reveals the relationship between engagement and enlistment: they are merely the 2 ends of a continuous spectrum. So getting to the next level of digital engagement may be easier than you think, because the next level of digital engagement is enlistment. So, just continue engaging your customers deeper and deeper, to the point that they become willing and want to help your employees and other customers.
*Image Credit: PublicDomainPictures and Didgeman.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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Welcome back to my mini-series on digital transformation (DT). This is the 3rd installment of this mini-series. Previous blog entries can be accessed via the following posts:
Successful Digital Transformation Must Go Beyond Digital to the People, Process, and Culture
Why Transform Your Business Digitally? — A History Lesson through the 4 Gears
In my last blog entry, we learned that technology-driven business transformation, such as DT, is not new and has happened throughout human history. History also revealed a defensive argument for companies to transform digitally. Companies must transform themselves to take advantage of the new technologies, otherwise, they won’t exist very long.
More importantly, we introduced the 4 gears model that could guide you through this transformation process. Companies must evolve from the operating norm that only focuses on 2 gears (i.e. acquisition and monetization) to the new model that also focuses on engagement and enlistment. Today, we will examine the 4 gears model in greater detail to reveal the offensive logic for companies to transform their business digitally.
The Ultimate Differentiator in a World of Mass Commoditization
As the market becomes more competitive, many products and services are being commoditized, where they become indistinguishable in the consumers’ eyes except their price. Therefore, companies engage in price wars constantly to stay competitive. This squeezes the profit margin of brands and threatens their business. Consequently, brands are struggling to differentiate in order to avert the commoditization of their products/services.
Today, many brands focus on customer experience (CX) as the ultimate differentiator. This is confirmed by a survey conducted by Gartner, which reports that 89% of the companies are expected to compete on CX. There are at least 2 good reasons for this:
Effectiveness: The most powerful and direct way to make consumers truly understand how your brand is different is to make them feel It’s much less effective to tell or even to show consumers how your products/services are different, because they won’t feel it. Besides, consumers will always discount what you said due to their inherent distrust in brands.
Irreplicability: Other competitors may be able to replicate your products/services, but it is operationally much harder to replicate the entire CX throughout the customer journey. Because CX can be affected in so many different touchpoints.
Digital transformation can help companies provide their customers a better CX. If done right, companies can even deliver a unique CX for everyone—a hyper-personalized experience that is optimized for a single individual. In order to deliver a hyper-personalized experience for everyone, brands must master 2 prerequisites as part of their business operation:
Collect enough data about their customer to understand each one’s unique preferences
Deliver a unique (therefore hyper-personalized) experience for each customer based on the individual’s preference data
Digital transformation can help brands realize hyper-personalization because it’s much easier to achieve both prerequisites above in the digital space. That is the digital advantage. Companies that engage their consumers digitally have an edge over those who don’t, because they can:
collect more data about their customers to understand them better
deliver a more relevant and personalized experience to their customers
Surely a hyper-personalized CX will help brands get more attention from their consumers, but it will also help brands win the engagement game and start spinning the engagement (and enlistment) gears.
The 4 Gears Create a Journey, Which Creates Experiences
Over the past century, businesses had much time to optimize the 2 gears model with many technologies that were invented along the way. Consequently, both the acquisition and monetization gears can spin very fast; they are so efficient that they can pretty much happen instantaneously. It only takes a few seconds to swipe your card, tap your phone, or simply click a button to monetize a consumer. Likewise, it takes anywhere from seconds to minute to capture a consumer’s attention with all the media impressions around us. Moreover, consumers expect acquisition and monetization to be fast, so most of them wouldn’t want to spend more than a few minutes with brands on these 2 gears.
The interesting question is, when do consumers ever seek out and want to spend time with a brand? There are actually 2 windows along the customer journey where this happens.
The pre-purchase window (a.k.a. the explore and evaluate phase)
The post-purchase window (a.k.a. the use and service/care phase)
If we map the 4 gears model to the customer journey, these 2 opportune windows correspond to the 2 new gears. During the pre-purchase window is when you should engage your customers, and during the post-purchase window is when you should enlist them.
By introducing the engagement and enlistment gears, the 4 gears model effectively created a journey for businesses that coincides with the customer’s journey. The implication is that brands that take engagement and enlistment seriously will gain many more opportunity to interact with customers throughout their journey. This is important because it serves as the foundation for companies to compete on the basis of CX in a market where everything else is being commoditized.
I must emphasize that a journey is what creates memorable experiences for people. Because acquisition and monetization happen so quickly, there is almost no time for any meaningful experiences to develop. Moreover, you can’t artificially slow down acquisition or monetization either, because consumers would feel that’s an unpleasant experience. By focusing on engagement and enlistment, you could create effective touchpoints that your customers want to interact with. And if you go further to improve the CX at those touchpoints, you could effectively redefine your customers’ experience with the brand.
Conclusion
As you can see, the 4 gears model is really a CX-centric model that helps sustain your business in the digital age. This model has already revealed a defensive reason for businesses to transform themselves digitally (see my previous entry). The logic is simple. They must in order to ensure the long-term viability of their business.
The offensive logic for digital transformation is also simple. Brands have 2 fates in today’s highly competitive world. They can either let competition drive their product and services to mass commoditization, or they can choose to differentiate. Since the ultimate differentiator is CX (due to their effectiveness and irreplicability), brands must transform themselves digitally to exploit the digital advantages that enable them to deliver the optimal CX—personalization.
Interestingly, the 4 gears model also provides the strategic framework that helps brands deliver a better CX. It does so by creating 2 extra gears that force modern enterprises to focus on engagement and enlistment in addition to the 2 conventional gears (i.e. acquisition and monetization). This effectively creates a journey for consumers to engage with brands during their pre- and post-purchase window. Consequently, brands that choose the engage and enlist will gain the golden opportunity to create the optimal (personalized) CX for their customers.
*Image Credit: Webrarian, geralt, and Kavworks Technologies.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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Welcome back to the 2nd installment of my mini-series on digital transformation (DT). From my previous vlog entry, we learn that DT is hard (~70% failure rate) because it’s a long process to changes the technology as well as the people, process, and culture any company. Despite the challenge, companies are lining up for this process, and there are both defensive and offensive reasons for them to do so.
Many digital visionaries already know the business imperative for DT. However, to gain company-wide support for such a colossal undertaking requires that everyone understand why you are doing it. Today, we will examine a defensive reason from a historical perspective, and next time we will discuss the offensive logic behind DT.
Before we dive in, I just like to emphasize that technology-driven business transformation, such as DT, is not new. It has actually happened before, and we can learn a lot from history. Moreover, a good way to foresee where we’re heading is to understand how we got where we are.
The 2 Gears Model of Modern Business
If we can roll back time about 150 years, how would business operate back then? That was a time where all trades and exchanges happened in one place, and that place is the market. All business transaction, whether it’s pricing or any product related inquiries, happen in the market, face to face, in real-time.
This has been the way of business for a long time. Until technology came and disrupted the market. First, advancement in transportation allowed businesses to deal with others much further away and expanded the market. Communication technologies further extend a company’s reach, and thus the market. They also enabled companies to operate much more efficiently, so companies scaled and grew as well.
While a big market and a big company are all great for business, it also created an artificial separation between the consumer market and the brands. Therefore, most modern day businesses operate under the 2 gears model—they need to acquire customers (marketing) and monetize them (sales/transaction). Companies focus on transactional efficiency, so the faster they can get people from acquisition to monetization the better they perform. Since businesses had over 100 years of practice in this, many large enterprises can do this very well.
Disruptive Technologies Demand New Business Model
However, technology innovation never stops. A new wave of social technologies came disrupted this way of business again. The disruptive power of social media was predicted in Clue Train Manifesto (published in 1999 when Facebook and Twitter didn’t exist yet) based on the simple thesis that the market has changed again. The new markets are conversations. But most conversations today are digital and take place on social channels. A logical response to this new market is to simply acquire digitally on social media. So, even though the market has completely changed, brands are still doing business as usual under the 2 gears model—they acquire and monetize.
The problem is that this is no longer sustainable in the age of social media. And the reason is that digital acquisition is very different from the traditional notion of customer acquisition. In the digital world, you don’t own what you acquire. A seemingly successful marketing campaign may acquire a million fans on Facebook, but you don’t keep any of those fans.
So what are you acquiring with the acquisition gear? In reality, this gear is merely acquiring people’s attention, so you haven’t acquired any customer until you monetize them. Although it’s true that you could monetize consumers while you captured their attention, consumers’ attention spans are short and they are getting shorter. The average attention span of consumers is ~8.25 second, which is shorter than the memory of a goldfish.
The implication of this for your business is that, even though you can monetize your audience when you have their attention, people’s attention shifts away quickly, so you have to spend the money you just earned from monetizing them to re-acquire them back. And you have to do this over and over again. That’s why marketers often find themselves running one campaign after another, and they have to do this again and again faster. Yet the campaigns are becoming less and less effective. That’s why this 2 gears model is no longer sustainable.
The 4 Gears: A Sustainable Business Model in the Digital Age
So can we make this model more sustainable in today’s social and digital world? And if so, how? The good news is that it is possible, but we can’t do it with just 2 gears. We need 2 extra gears, which bring us to the 4 gears model originally proposed by Geoffrey Moore in his famous book Crossing the Chasm.
The first of the 2 extra gears is engagement. At a high level, engagement helps you sustain monetization, because when you engage customers, you capture their attention longer, so you can monetize them longer. In reality, engagement helps you build stronger relationships with your customers, so they become loyal customers. Since loyal customers, by definition, are those who will continue doing business with you, this is how you can monetize them longer—through their loyalty.
The second extra gear is enlistment. Enlistment is still a novel concept in business. In short, it’s leveraging your customer to help you do work that’s normally done within your enterprise. A simple example of enlistment is advocacy, because you are leveraging your customers’ word of mouth to help you do marketing, which is normally done by marketers within your company. There are many other ways to enlist customers.
Enlistment helps you scale your acquisition because there are usually a lot more customers than you have employees in most businesses. Moreover, leveraging customers is also more effective because consumers trust other customers more than they trust brands.
When all 4 gears are spinning, you create a viral loop that feeds back on itself. Since this viral loop is a positive feedback loop, it will be sustainable if we can get all 4 gears to spin. The caveat is that if anyone of the gear slows down or stops, the whole viral loop can’t go faster than the slowest gear. So business must again learn to organize themselves in ways that allow them to spin all 4 gears simultaneously.
Conclusion
It used to be the case that businesses only have to focus on one thing—go to the market and sell. This is basically the 1 gear model that focus solely on monetization. Then transportation and communication technology disrupted this way of business by allowing businesses to do more. So companies have to learn to take advantage of these technologies and really do more with them (i.e. more than just monetization). Over the past hundred years or so, companies have learned to do more with these technologies and operate under the 2 gears model.
Today, many enterprises are organized in ways that allow them to spin both the acquisition gear (marketing) and monetization gear (sales/transaction) synchronously. This 2 gears model has become the new norm today, and it would be absurd to tell any growing company to stop their marketing operation. As history confirms, companies that leveraged technology and did more, also gained a substantial competitive edge in the market, because most companies that did not learn to take advantage of those technologies are not around anymore.
Today, history is repeating itself as social media disrupts the market. Social media allows us to do more, but we have to learn to take advantage of it and do more with it. That means we can’t just focus on the 2 gears (i.e. acquire and monetize). We must also use it to engage and enlist customers. Businesses must learn to operate under the 4 gears model and simultaneously spin all 4 gears: acquire, engage, monetize, and enlist. Those that do, will again have a competitive edge, but those that don’t, will be left behind while others that do move on.
*Image Credit: Gerd Leonhard, geralt, and Pexels.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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Hello @ArturF,
I'm glad to hear that my comment opened your mind. And also I am glad that you changing your thoughts about Niantic.
I know it's hard to believe that companies will actually listen to the customers in light of all the recent crisis with United Airline. This speaks further to the importance of customer centricity in this age of digital media. Traditional companies that are self-serving will eventually extinct if they don't learn the rule of the game in the digital age.
However, I do think that there are some companies that get this and genuinely want to deliver what customers want. It may take sometimes to turn the customer voice into market deliverables, but they genuinely want to do it. I think this is the case with Niantic.
That said, I also think that there room to improve, and that is on the communication front. They could definitely do better to update the consumer about the status of the release leading up to the actual release rather than going radio silence and then a big PR.
Finally, thank you for remembering me and reminding me of that HP Global Meet Up in SF 2015. I do remember that event. Thank you for all your support and interest in my work.
See you again next time.
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Hello Terry ( @misterrosen ),
Thank you for raising your concern and such an interesting questions.
I apologize that I could not respond earlier. But now, I finally got the opportunity to respond to some loose-ends conversations on my blog. So let me try to address your concern.
First of all, I am familiar with both Alfie Kohn and Edward Deming, and I have deep respect for their work respectively. But as you said, we live in a society where traditional evaluation is deeply ingrained. So it may just be a necessary evil for now, which can only be undone over time. It will take time to undo the wrong system we put in place due to our own ignorance. So we all have to walk a very fine line between the modern view (of Deming and Kohn) and the more traditional views.
Second, I don’t believe there is any contradiction in what I said with Kohn and Deming. The grading system is what’s destroying the intrinsic motivation, not the student’s desire to do better. He or she is simply a victim of the system that we put in place, because we train him/her to believe that getting a good grade is a good thing, because the society will reward him/her for it. Given the right environment, we all will try our best to optimize our gain. In this respect, a student wanting to get a good grade is no different from someone trying to get some food because he’s hungry. Having desire is not bad, it’s human nature. The system is what’s flawed.
Third, I do believe that both intrinsic and extrinsic motivation has its place. They both exist for a reason. Biologically and evolutionarily, we and other animals respond to both because they are both important for our survival. It is not the case that intrinsic motivation is always better than their extrinsic counterpart. It really depends on what you are trying to accomplish. Good leaders should know how to leverage both. Unfortunately many leaders today are too focused on the extrinsic, because they tend to work faster and have more measurable effects.
Finally, you are right that I probably have made an assumption that “nothing is wrong,” when I said “there is nothing wrong with wanting to get good grades.” But I felt that it’s a fair assumption, because we can easily find millions of things that is wrong with our world. If I want to be critical about it, I can easily go down the path that everything is wrong and we might as well reboot this entire world that we lived in. And even then there is no guarantee that the new system won’t create other problems.
One could easily argue that Kohn and Deming are wrong, because they publish their work to promote their selfish belief, for fame, and potential economic gain. We are too, because we have a job and trying to do things for our own economic gain, which is extrinsic too. Why do we accept payment? So is the economic system is wrong? Is capitalism is wrong because it’s not fair? But a purely utopian type of community is not perfect either because it kills motivation and encourages people to do the bare minimum. IMHO, I believe that everything that we do or said in this world is based on some assumptions. And these assumptions aren’t bad or wrong. They are merely our past experiences that shape our lives, our thinking, even our beliefs.
Thank you for your comment. It’s well taken. I don’t take disagreement personally, because I see it as an opportunity to further our knowledge. Besides, I like these academic debates, even though they could be very wrong and wasteful of earthly resources in someone’s eyes, since they often do not produce anything of value. But who is to decide what’s valuable? Isn’t that subjective? So if I am happy doing it should that be enough justification? But isn’t that just selfish? Why is this form of selfishness OK, and the student working by themselves and don't want to help other is not?
See the point? I don't think we can blame people for their sub-optimal behavior if the system is not perfect. But we can't blame the system either because they are created by less than perfect human beings just like ourselves.
Some of the problems we face are very complex. I don’t think we as a human species collectively are even close to having any real solutions to some of these problems we created. I can only hope for the best.
I hope you enjoy this discussion. I did.
See you next time.
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Hello @ArturF
Thank you for the comment and inquiry. My apology for taking so long to reply.
For that, I'll give you this image composite. Hope you get a crack at it.
You are right. The hype for PokemonGo is over and the player base did go down. And that is expected, even for great games that are designed very well. There are very few games that can really stand the test of time.
But let me clarify a point before commenting on the game itself. Whether a game is well-designed or not has little to do with whether your business is well-ran. Although one can affect another, they are certainly not the same.
First, let’s look at the business.
In the case of PokemonGo, I still think it is a fairly well-designed game that is pretty innovative for its time. That is not to say that it doesn’t have any problem (more on this later). However, the business isn't run very well, both strategically and operationally.
Although I don’t know the detail operating constraint of the company, so it is hard to understand exactly why they don’t have a customer-centric strategy (i.e. don’t communicate with customers and don’t respond to their frustrations and needs). As you mentioned, maybe they want to, but cannot keep up with what the community is asking for. In that case, it would be a business operation problem.
IMHO, I think that in this age, it is more important for companies to have a customer-centric strategy than to have a killer product initially (if we only consider these 2 factors). Because companies that are customer-centric (but don’t have an innovative product) will learn from their customer and slowly improve their product to really meet the needs of the customers. Over time, they will eventually become more successful than companies that have an innovative product initially (but are not customer centric).
Unfortunately, Niantic was the opposite. They had an innovative product, but because they did not focus on being customer-centric, they can’t keep with the evolving needs of the customers. So even if they were successful initially, they will eventually fall behind compare to companies that do not have an innovative product initially.
Now, keep in mind that this is an overly simplistic view, because in real life, there are many more factors involved. There is competition, global operation constraints, global economics, etc. But if all things equal (which is rarely the case), I am saying that a customer-centric strategy is more important than a killer product initially in today’s world. Certainly, Niantic did not do well on this as a business and a company.
Now, let move on to the game.
As I said earlier, PokemonGo is a fairly innovative game. But from a gamification/behavior science perspective, it can certain do much better. For one thing, it violated one of the tenets of successful gamification I wrote earlier: The Better it Work, the Faster you Must Change. PokemonGo worked very well initially. It was an instant hit when they launch (they became the top grossing app in the US in 13 hours, and Nintendo’s Market value increase by $9 Billion in 5 days). However, they did not change fast enough. It’s more of the same game over and over again, so that was the beginning of the fall of their user base.
So, the moral of the story is that if you can’t change fast enough (for whatever reasons), it may be better to limit your release in phases, so it makes a smaller splash initially in exchange for the longevity of the game. Unless a big bang and a then a crash is what you want, of course (and surprisingly, sometimes people really want just that).
In terms of the design, PokemonGo has excellent onboarding, but they didn’t do so well for the scaffolding and the end game. They could have leveraged the principle of baby step and unlocking more effectively. Rather than making users collect all 151 (initially, they subsequently added more) of them at the beginning, break it up into smaller groups. That way it’s more achievable and players can get more frequent feedbacks and so they continue to play longer.
There are many ways to break up the game into many mini-games (levels). But the important point being that by designing more levels and milestones in the middle of the game, they can engage the players much longer. And players won’t suffer as much from the fatigue and boredom of getting the same kind of feedback from the same game over and over again.
Alright, this reply is getting a bit long, so let me take a pause here.
I hope this addresses your question.
Thank you again for the question and your interest, and hope to see you again next time.
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Hello @jjsgva,
Thank you for asking, and my apology for the much-delayed reply.
And thanks, @RobbL, for bring my attention to this inquiry.
So let me try to address the question between the difference between community and social networks.
First of all, I’m afraid to say that there is no academic reference to reference for the specific distinction I made between communities and social networks, because I believe I was the first to characterize the difference this particular way. However, I didn’t just make them up out of thin air. There is an academic paper trail, and very good reasons for my particular definition of community vs. social network, which is based on their mathematical structural properties. However, this mathematical/structural definition does require a bit of technicality and a bit of background knowledge in sociology and network/graph theory to fully understand. So, to avoid creating more confusion with an esoteric definition, I decided to create a definition that is much easier to understand by the layman, that is fully consistent with the more rigorous mathematical definition.
If you bear with me, I’ll go through the logic with you.
As I said earlier, the particular distinction I used between community and social network is a structural one. Keep in mind that there are many other ways to distinguish these 2 social structures (e.g. use case, technology, business applications, or even public perception, etc.). No one is particularly optimal, but some are better suited for different purposes. However, I choose to use their structural properties because they are general and more fundamental to the way people communicate.
The critical concept to understand is transitive closure (which is related to the more familiar triadic closure property in sociology).
Community is a social structure that satisfies the mathematical property of transitive closure. If [person A] is in the same community as [person B], and [person B] is in the same community as [person C], then [person A] will be in the same community as [person C].
Social network, on the other hand, breaks transitive closure (does not satisfy transitive closure). That is, if [person A] is connected to [person B], and [person B] is connected to [person C], then it is not necessarily the case that [person A] will be connected to [person C].
This structural property limits how members of the community/social network can communicate and therefore interact.
In the case of a community, there is no structural communication barrier between [person A] and [person C]. That means in a community, anyone CAN talk to everyone else as long as they are in the same community. A YouTube user can talk to anyone on the YouTube platform. This doesn’t mean that everyone WILL talk to everyone else, because there may still be other barriers to communication (e.g. social, behavioral, psychological, geographic, linguistic, and even political) that prevent people within the same community from communicating. These are very interesting in their own right, but beyond the scope of this reply.
With this definition, many social media channels are really communities, and they are typically referred to, in academia, as communities of interest.
In contrast, a social network will have some structural communication barrier between [person A] and [person C]. If they are not connected, they CAN’T communicate or interact as freely. Exactly how much interaction and communication is allowed will be determined by the particular platform. But there will be some structural barrier. For example, you can’t message people on Facebook unless you are connected to them first, but you can still view their public profile and photos, etc.
In this definition, Facebook, Linkedin, as well as many mobile messengers (e.g. WhatsApp, Line, WeChat, etc.) are structurally social networks based on this definition.
See the distinction between them?
As I mentioned earlier, the transitive closure property is related to the more well-known studied triadic closure property on social network. Triadic Closure simply says that if 2 persons (e.g. [person A] and [person C]) are connected to the same person (e.g. [person B]), then there is a higher probability that they are connected to each other. However, this connection is not guaranteed, otherwise, we have transitive closure, in which case we’ll get a community.
In many classical sociology literatures that study social networks, the part of the network that has a significantly higher degree of closure is precisely what people defined as a community (Coleman 1998). Basically, regions that are more densely connected (i.e. have a higher degree of closure or triadic closure) than their surrounding in a social network is what constitutes a community and what people defined as a community. This is not just a theoretical exercise, as people have developed community detection algorithms that use this property to operationally discover communities within a social network (Newman 2006).
OK, I hope this convinces you that there is truly a structural distinction between community and social networks, and this is documented in academic literature. But as you can see, this does require quite a bit of explaining, and it’s probably overly academic for most business audience.
So I’ve decided to create a simpler definition that is fully consistent with this academically rigorous definition. This is what lead to my definition in the post that characterizes the difference between community and social network based on what held them together. This definition is consistent with the mathematically rigorous definition because they are virtually equivalent.
A community is held together by a common interest. So if [person A] have the same interest as [person B], and [person B] have the same interest as [person C], then [person A] will have the same interest as [person C], satisfying the transitive closure property. So they will be in the same community and thus won’t have any structural communication barrier among them.
Social networks, on the other hand, is held together by interpersonal relationships. Given that [person A] has a personal relationship with [person B], and [person B] has a relationship with [person C], this does not guarantee that [person A] has a personal relationship with [person C], thus breaking the transitive closure property, as social networks should (which creates a structural barrier to communication).
This came out much longer, but the short answer to your original question is, NO. There aren't any academic papers that you can cite that characterizes community vs social network based on what held them together. But there is a very rigorous and academic reason that I define the difference between community and social network the way I did. I hope you can use this resource to help further your study of community vs social network.
Thank you for your interest.
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With technology continually evolving and changing, so does its vocabulary. The enterprise world is littered with jargon, one of the buzzwords du jour being “digital transformation (DT)” which I’m sure you’ve heard of by now. But what does it mean? It’s like Dan Ariely’s humorous comment on big data, “everyone talks about it, nobody really knows how to do it, but everyone thinks everyone else is doing it, so everyone claims they are doing it.”
At a high level, DT is very easy. It’s simply the adoption of digital technologies to transform your business. So just choose the digital technology you want, and use it to change how your business operates. Done!
Why Digital Transformation Fails
Sounds easy. But it’s not. Numerous sources report that roughly 70% (ranging from 66% to as high as 84% via Forbes) of the DT initiatives fail. Clearly, it can’t be as simple as deploying a digital technology (given such high failure rate), even though that could pose challenge in some cases.
So why is DT so difficult? The reason is because that a true transformation of your business requires more than just the adoption of new technology. DT usually starts with some kind of technology upgrade, but that’s only the first step. Subsequently, it requires changes in your business processes, your employee and leadership behavior, and ultimately your corporate culture. Changing technology might be easy, but changing the people, processes and culture is hard.
The challenge of DT is not a digital or even a technological problem; it’s a business transformation problem. If we try to understand why DT fails, the most common causes of failure boil down to the following 4 categories of reasons.
Technology:
Using outdated technologies
Failure to integrate with legacy or other digital systems
Believing that it’s only a technology problem
People:
Lack of clarity and vision
Lack of leadership support
Too much top down imposition without grass root support
Lack of a digitally savvy workforce
Process:
Silo effort that didn’t engage the broader stakeholders
Process misalignment
Not agile enough for faster innovation
Culture:
Short term thinking
Not customer centric
Too little cross-functional collaboration
Since these are failure modes, they are all important. As it only takes one broken link to break the whole chain, any one of these failure modes could undermine the success of your entire DT initiative. So every one of them must be addressed, which is a lot for businesses to undertake.
But here’s the bright side: Although all the common failure modes must be addressed, not all of them need to be addressed at once. And if you are embarking on the DT journey, not all of them need to be addressed at the beginning. So which ones should you focus on first?
Upon analyzing the natural dependency among these failure modes, there are only 3 that must be addressed from the get-go. And I will explain this with the video blog below.
1) Customer Centricity
A customer-centric strategy is imperative, simply because every business needs customers. Moreover, in an increasingly service-oriented subscription economy, every business is striving to retain their customers, because not only is the competition more intense, the switching cost for consumers is often minimal. While this is a given from a business standpoint, customer centricity is equally as important for your digital transformation (DT) initiative for several reasons.
It’s easier to rally for support when you have a customer-centric strategy, precisely because it makes business sense. Very few people would argue against serving your customers. A well thought-out customer-centric strategy could easily win both leadership and grassroot support. You still need to sell the strategy within your enterprise, but it shouldn’t be a difficult sell.
It’s also less challenging to create processes that are aligned across different departments with a customer-centric mindset. Traditional business processes are often created to optimize some business KPIs while meeting their operating constraints. However, different departments and teams often operate under disparate constraints and have unique set of KPIs. Consequently, their processes are typically misaligned because they were created irrespective of one another. Customer-centricity serves as the glue that binds different departments and teams together. It helps you create processes that are aligned with giving your customers a great experience.
When all your processes are aligned, it facilitates cross-functional collaboration. At the very least, the processes are not adding friction that could hinder collaboration. Although this doesn’t automatically drive collaboration, it certainly makes it easier when there is a business need to do so. When that happens, your DT is suddenly no longer a siloed effort.
Finally, a customer-centric mindset fosters long-term thinking because most businesses want to have loyal (long-term) customers, especially in a subscription economy.
2) A Clear Vision
Despite the simplicity of the definition, digital transformation (DT) could be confusing because it’s different for every company. Myriads of digital technologies are on the market, which can change any one of the multitude of business operation within your enterprise.
For example, DT for one company may be using iPads (a digital technology) to scale onboarding of new employees (a perfectly valid HR function). It could also be using social media (another digital technology) to engage and support your customers throughout their customer journey (a marketing and customer support operation). It could even be using big data (yet another class of digital technology) to predict sales, using IoT and augmented reality to improve customer experience, or anything in between.
DT can mean many different things, so you must have a clear vision of what DT means for your enterprise. Which digital technology are you using? And which part of your business operation are you trying to improve with these technologies initially? Most importantly, what business outcome are you trying to achieve? As alluded earlier, a customer-centric mindset could help you answer some of these questions and shape your vision.
Armed with a clear vision of what DT means for your business makes it even easier to garner both leadership and grassroot support. And if you are a leader, a clear vision probably means that you are bought in and committed to supporting this change.
3) The Right Technology
Since digital transformation (DT) almost always starts with a technology upgrade, it is important to choose the right technology at the beginning. Having a clear DT vision that is customer-centric helps you choose the digital technologies to realize your vision, but there are other factors to consider.
Certainly, the right technology must have all the functionality required by your specific DT project. It must meet all the security, reliability, and legal compliances for your enterprise, and must built to scale with robust technologies that last. This is unique to each business, but there are two elements that are often overlooked at the beginning which may impact the long-term success of your DT initiatives.
First, the right technologies should be easily integrated into with the rest of your company’s technology ecosystem. And that includes both your legacy systems and other newly adopted digital systems. Keep in mind that when you kick off a digital initiative, your core business will still be running on your legacy system. Failing to integrate with these systems means your DT project will remain a siloed effort. While DT initiatives often start small in one area of the company, it must permeate throughout your enterprise to achieve lasting transformation.
Second, the right technologies should be simple and intuitive to use. It should be so intuitive that even your non-digital workforce should be able to pick it up and immediately carry out rudimentary functions without much training. Of course, training and education will always be required to reach proficiency.
The key is to make sure that the learning curve does not offset the efficiency gain from the use of your new digital technology for the “digital novice,” even at the very beginning. Furthermore, when there is residual efficiency gain, even during the adoption phase of your DT project, innovative minds within your enterprise will have the cognitive surplus to innovate and be more agile.
Transformation means lasting change
Digital transformation is a journey. It always starts with the adoption of digital technologies, but it must also change the people, process and the culture to be truly transformative. It typically begins as a siloed technology project, but must permeate throughout your enterprise. Although digital transformation can seem difficult, concentrating on the above focuses at the very start will help pave the road for long-term success.
*This article originally appeared on CMSWire.
*Image Credit: Pexels and tpsdave.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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We’ve been told that we are special ever since we were little, but how special are we? I challenge you to find another person in the world that is exactly like you. Such person probably doesn’t exist. So if that is the case the question is, why is our brand experience often identical to everyone else’s? Somehow, in the eyes of big brands, we are just like everyone else.
The Look-Alike Illusion
Let’s try to understand why that’s the case. If you were to find your double, how would you do it? You may start the search based on some personal attributes like, gender, age, ethnicity, etc. However, as you include more of these demographic dimensions (e.g. religion, education level, political orientation, etc.), you will discover that fewer are like you (i.e. matches you in every one of these dimension).
So the apparent similarity among consumers is merely an illusion that arises from the lack of data. We only look alike because brands don’t have enough data to distinguish us uniquely. With more data, brands can see us in 3D and recognize our uniqueness.
Every one of your customers are unique, just like you. They all have different needs, limitations and preferences. They all deserve a unique brand experience. The question is, how?
In order to personalize the experiences of your customers, brands must overcome two major challenges:
Brands must understand their customers at a personal level. This requires huge amounts of personal and behavior data about their customers to uniquely distinguish them all, which can be difficult to obtain.
Brands must deliver a unique experience for each customer base on the understanding of their individual preferences. This is even more challenging, because most brands operate at scale for efficiency (due to the economy of scale). Such individualized offerings are very difficult to scale.
The Digital Advantage of Big Data
Personalization is hard, because traditionally many brands don’t have enough data to understand their customers at a personal level, let alone deliver a unique experience.
Big data changes this. For the first time, brands have enough data to distinguish one customer from another. Beyond the demographic dimensions on which traditional segmentation is based, brand now have access to hundreds and thousands of social and behavior dimensions. So brands have enough data to overcome the first personalization challenge.
This is why brands like Amazon and Netflix is able to hyper-personalize (i.e. personalize to one single individual) and offer a truly unique experience to each customer. However, many brick-and-mortar retail brands seem to be lagging behind in their personalization efforts. This is not surprising, and there are good reasons for it. Digital-native brands have a huge advantage in overcoming both challenges of personalization:
In the digital world, it is much easier to collect lots of behavior data from the consumers. Digital-native brands can easily track what product you searched, browsed, bought; what movies you rated well, what movies you watched, how much time you spent on researching a particular product, and other behavior. However, it is much more difficult for brick-and-mortar shops to obtain data on who visited the store and what items they browsed, tried on or other actions.
Not surprisingly, it is also much easier to deliver a unique experienced in the digital world. This is because most of the digital environment is controlled by software and can be customized by data learned from a consumer’s behaviors. Everything from which product you see, to what background color is used in the e-store can all be customized by a customer’s preferences and few lines of codes. In the physical world, it would be impossible to manipulate the experience suite each and every individual.
This is one of the reason why so many companies are talking about digital transformation today. Because the digital-native enterprises are able to give their customers a much more personalized experience, they will win the long-term engagement with their customers. This not only enables them to acquire customers faster, but also retain them much more effectively. This is vital in an increasingly competitive market for people’s limited attention.
Ubiquitous Sensing via IoT
Due to the digital advantage, retail brands face serious challenges from their digital competitors. But this is all about to change. The Internet of Things (IoT) will enable a whole new level of behavior data collection like never before. When physical “things” in this world are able to communicate with each other, it offsets the advantages that digital brands have in understanding their customers. A pair of jeans on the shelf may one day know which mobile device is looking at them, which picked them up, and which actually tried them on.
Not only does IoT enable the collection of behavior data in the physical world, it also enables the collection of rich environmental metadata, which gives the context and meaning to the behavior. The metadata gives contextual cues that help brands understand why a consumer behaved the way he or she did, rather than just the fact that he or she did something.
For example, knowing that I bought a GoPro is one level of understanding, but knowing that I bought it with my niece for her birthday should completely change the way brands market future product to me. I wouldn’t see any irrelevant ads on GoPro accessories for a camera that I don’t own. Instead, an annual reminder of my niece’s upcoming birthday, and potential accessories for young women as birthday present can go much further.
Merging the digital behavior data with the physical give us a more complete 3D view of our customers. This will help us further personalize the experience of our customers at every touchpoint along their consumer journey.
The Retail Strike Back with AR
The ability to measure, track and understand customers at a personal level is only half of the battle. The other half is even more challenging for brick-and-mortar retail brands: delivering a unique experience based on their understanding of the customer’s preference. This is difficult because manipulating physical spaces and environment to meet any individual’s preference is nearly impossible. That is, until augmented reality (AR), which has been popularized by Pokemon Go.
Tomorrow’s consumers do not have to see the physical world as-is, they can overlay it with digital layers. Although it’s impossible to customize the items on a physical shelf to suite everyone’s preference, it is possible for AR to recognize items that are relevant to consumers and direct their attention to them. While it is not realistic to paint the wall of the retail space with everyone’s favorite color, AR can overlay the walls with any color or even background of a customer’s choosing.
AR provides brick-and-mortar brands with a digital layer on top of the physical world. This offsets the advantages that digital brands have in delivering a unique experience again. Just as in the purely-digital world, this digital layer is controlled by software and can be customized by code and as much preference data as consumers are comfortable providing.
Although the digital-native brands have a head start in personalization, technological innovations such as IoT and AR, will even the playing field. Eventually, every brand will have the capability to hyper-personalize their experience for everyone. Personalization is not merely a set of technologies, it is a customer-centric business strategy that recognizes the unique context of every individual customer.
Brands, digital or not, must learn to consider individual preferences in order to win customers’ long-term engagement. Besides, brands have no excuse not to provide us a personalized experience. All the required technologies already exist, for brands in both the digital and physical world. With this, perhaps one day we may have a personalized shopping experience like those in the movie Minority Report.
*This article originally appeared on CMSWire.
Image Credit: Unsplash, geralt, and Keiichi Matsuda.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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After a deep dive into the inner workings of one class of prescriptive analytics—recommender system (personalization engines), it’s time to step back and explore how we can leverage prescriptive analytics in general. Today, I am going to outline 3 relevant use cases:
In business, prescriptive analytics can help you optimize business processes and operation
In social business, it can help your social media team focus on what drives the biggest business impact
In social media, it can even help you optimize your engagement time
These are very distinctive use cases, where the objectives being optimized are drastically different. I hope these examples will not only demonstrate the power and versatility of prescriptive analytics, but also give you a better understanding of how they work. (If they don’t please feel free to let me know!)
Optimization of Business Process and Operation
I’ve discussed before that the simplest example of prescriptive analytics is a GPS, which operates in the geospatial domain. In general, prescriptive analytics are not limited to geospatial optimization. We can optimize processes and other parts of business operations. One of the most common use cases of prescriptive analytics is business optimization. Since the underlying computation of prescriptive analytics is already an optimization of some objective, this application is very natural. In this case, the objective we optimize is typically the efficiency or throughput of the process.
Whether you are optimizing a business process in marketing, sales, or customer service, you must tell the prescriptive analytics system what you are trying to achieve. This is like telling the GPS where you are going. For example, increase conversion by 10%, increase sales by 20%, or increase your net promoter score (NPS) by 5 points. These are the goals, or “destinations” you are trying to reach in a non-geospatial domain.
Subsequently, the prescriptive analytics system would prescribe a sequence of actions that lead to the corresponding business outcomes you want (i.e. increase conversion by 10%, sales by 20%, or NPS by 5 points). For example, to achieve 10% conversion lift, the system may prescribe reducing the frequency of your email marketing by 35%; simultaneously increase your real-time social media engagement by 30%; and when your real-time engagement reaches 15%, start directing people to your customer community for peer-to-peer engagement and recommendation. These are like the turns that your GPS system advises you during the journey, except these directions are not in the geospatial domain.
Optimizing Business Impact of Your Social Media Engagement
Now you know what prescriptive analytics can do for your business, let’s dive deeper on how it prescribes actions. The key lies in the objective that’s being optimized. Let’s examine a couple of examples from social media.
These days, social media is not new to business, but many enterprises still struggle to figure out how best to leverage it. Because there are so many different things you can do on social media, it’s hard to determine where to best allocate your limited resources (e.g. time, money, energy, etc.). Should you create more YouTube videos or should you use Snapchat? Should you publish more blogs, or participate more in the Q&A section of your community? The social media landscape is complex with thousands of social channels. Even within a single social channels, there are probably a handful of actions you can take. Take Twitter for example. It’s probably one of the simplest social channels out there, but you can already engage in many different ways: tweet a message, reply to one, retweet it, favorite it, follow someone, or simply read the tweets coming out of the firehose. This gives rise to many different social metrics that quantify how you engaged on social media.
Prescriptive analytics can help you focus on what you should do to achieve the biggest impact, but you must tell it what kind of impact you are looking for. Whether it’s increasing marketing conversion, sales, or customer satisfaction (CSAT), you should be able to measure the impact you want to drive. This is typically a key performance indicator (KPI) for your business. Once you have the KPI that you are trying to drive, and the social media metrics that describe how you engage, it’s relatively trivial to perform a time-lagged cross correlation analysis to see which social metrics have the strongest correlation with your KPI. Although correlation is better than flying blind, it would be better if we could establish causation as well. But this would require less trivial methods in statistics or econometrics (e.g. instrumental variables). If you are comfortable with these advanced techniques, you can even establish causal relationship and identify the strongest causal predictors for your KPI.
The correlation strength (causal or not) between the social metrics and KPI is the objective. Upon maximizing this objective, the prescriptive analytics system would be able to prescribe the actions that are most effective at driving your KPI. This helps you focus your effort on a few actions that will give you greatest impact (as measured by the KPI). And if you choose a different KPI, the system will prescribe a different set of predictors that maximize this objective.
When to Post on Social Media—Engagement Time Optimization
Because social media engagement is voluntary, people can participate anytime they want. But when is the best time to participate? The answer really depends on what you are trying to achieve. The goal of most social media participation is usually to reach the widest audience (whether that’s for a brand or for us personally). Even when you are posting a question and want the fastest answer or the most accurate answer, these goals can often be achieved indirectly by maximizing your reach. By reaching the widest audience, you increase your odds of reaching someone who can respond immediately; and by reaching the widest audience, you also increase your odds of finding someone who has the expertise to address your question accurately.
There have been numerous studies on when is the optimal time to post on social media, and many infographics provide general guidance on when to post on various social channels. In general, these studies are rather limited because the data is highly aggregated, the sample size small, and the methodology not rigorous enough. We recognized that there really isn’t a universal best time to post on social media, because the best time to post is ultimately specific and unique to the individual.
Our brilliant data science team (humble brag!) have analyzed over a billion posted messages and observed reactions and found that the best time to post depends strongly on your specific audience’s engagement profile. Meaning when your specific audience is most actively participating on a particular social channel. Keep in mind that it is your audience’s behavior (i.e. how they choose to use social media) that determines the optimal time to post for you. Since we all have different audience on social media (e.g. different followers, friends, connections, etc.), the best-time-to-post for you may be totally different from the best-time-to-post for me. This is precisely how Lithium Reach is able to recommend the best-time-to-post that is hyper-personalized to a person or a brand.
Now, I could certainly tell you more about the product, but that’s not my style. However, if you have specific product related questions, I’d be happy to discuss, or invite more qualified product management staff to chime in.
We further validated the optimality of our hyper-personalized recommendation on a sample of half million active users and more than 25 million messages observed over a 56-days period. We found that our individually optimized post-time leads to an average of +17% engagement lift on Facebook and +4% on Twitter. This is a VERY conservative average. In practice, we have seen more than +50% engagement lift at times from brands using Lithium Reach’s recommended time-to-post feature.
Conclusion
We discussed 3 business use cases of prescriptive analytics. In each case some objectives are optimized, so the system can prescribe a few actions (or sequence of actions) out of infinite number of possible action you can take.
By maximizing the efficiency or throughput of some business processes under appropriate resource constraint, we get a prescribed sequence of steps to help us optimize the process.
By maximizing the causal correlation strength between social metrics and your desired KPI, we get a prescribed set of social metrics we should focus on driving. Although there are hundreds and possibly thousands of social metrics, this set of metrics is most effective at moving the needles for your KPI.
By maximizes the engagement profile of your specific audience (e.g. follower, friends, connections, etc.), we get a set of personalized recommendations on when to post on social media. Although you can post social media content at any time you like, posting at the recommended times will maximize the reach of your messages.
While optimization is something that computers can do very efficiently, doing this at the individual level is still a challenging big data problem. Because people’s social interactions change continuously, we must constantly re-optimize as the new data arrive from the respective social streams. Fortunately, our data scientists have done all the heavy lifting and built this hyper-personalized post time recommendation algorithm into Lithium Reach. Now, you can get the benefit of personalized recommendation on when to post without needing to routinely analyze billions of message and their reactions.
As you can see, prescriptive analytics is versatile and powerful. It has just as many applications in science and engineering as in business and social media. Next time I’ll share a somewhat esoteric use case of prescriptive analytics in data science. Yes, data scientists like me also use prescriptive analytics. Come back next time to find out more!
Image Credit: geralt, geralt, and valentinsimon0.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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Hello @ivan73ganchev,
My apology for the late reply. I just got back to SF from a long work trip.
The problem you mention is well-known the gamification community. It is called gamification fatigue. I discussed this in an earlier post: The Gamification Backlash + Two Long Term Business Strategies. I recommend you take a quick read there.
But in short, badge/point fatigue could potentially happen if gamification practitioners today are only thinking short term gamification. For example thinking only to drive behaviors for to benefit their own business. We need to think longer term than that. We should think of gamification as a tool to drive behaviors that are beneficial and has value to the user or are align to their intrinsic motivation. In these case, when the user have participate enough to realize the value, the value becomes the primary behavior driver (or the well aligned intrinsic motivation).
This is why I often proposed that we need to think about Sustainable Gamification: Playing the Game for the Long Haul. This is another related post that I would recommend you reading.
Have fun and see you around at my blog in the future.
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Hello @Jochen,
I think a little pressure is good. It does get me motivated about writing knowing that people are waiting on them. So Thx. And I will put on my writing hat once I am done with all the external speaking at conferences and lecturing at Universities.
I'm not sure if there is any easy way to subscribe and get notification to my speaking events. I think that if you follow my tweets and blogs, I should have some mention about upcoming events. Sorry there isn't any simpler ways that I can think of.
But really appreciate your interest and support.
See you again on my blog.
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Hello @Jochen,
My apology for the late reply. I've been on the road quite a bit lately and didn't have time to respond as timely to my blog comments as I'd like.
The shift from extrinsic to intrinsic is a very challenging topic. It's difficult to understand and rather difficult to write as it requires some background info on how people learn and internalize data and turn them into beliefs.
Short answer is that I've got the time to write that up yet. I will eventually. I just need a good chunk of time to sit down and write. So... another apology from my end.
But I will make a note of that and try to write a few more gamification blog post intermittent throughout some of my big data/data science posts. And I will try to write in such way that it leads up to that shift. OK?
Thank you for being supportive of my work and taking the time to comment.
See you again at my blog.
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Hello @skwilder,
Thanks you for taking the time to comment, and glad to hear that you confirm my observation too.
It is easier to see why companies keep falling into this pattern again and again. Because most companies are so risk aversive and do not want to do anything new and innovative. I hope this will change in the future.
See you again soon at this blog.
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From my previous post, we learned that despite how much we segment and bucket people, each of us is pretty unique. The reason that some of us may appear similar to a brand is because brands typically don’t have enough data to tell us apart. Big data changes that fact! With social and behavior data, we will have enough data to view a user in over hundreds of different dimensions. With so many dimensions, the chance of finding 2 matching individual along all these hundreds of different dimensions is highly improbable. We are truly unique!
The fact that we are unique demands personalization, but this is traditionally a very challenging data science problem. In fact, it’s prescriptive analytics. Last time we discussed 3 early approaches to personalization and the challenges they face.
Ask the user to self-declare their interest and preference
Learning from the user’s own past behaviors and recommend similar content
Learning from other users’ behaviors (e.g. traditional collaborative filtering) and recommend content from the collective interest of others like you
So how do we (Lithium) and others (e.g. Amazon) tackle these problems.
Since people’s interests are collectively pretty unique, we generally cannot infer a person’s interest based on the interests of other look-alikes. Because there really aren’t any look-alikes in the face of big data. This means traditional collaborative filters (CF) that rely on learning and extrapolating from other users will not perform well when inferring a person’s interests. Without an accurate understanding of a user’s interest, the recommended contents will not be truly personalized.
In order to ensure a highly personalize experience for a particular user, the recommender system should only use data from that user, and not generalize across different users. However, the majority of users will not have enough existing data on the platform for the recommender system to leverage, so initial recommendations are poor (i.e. cold start), leading to a poor customer experience and ultimately abandonment of the platform. So how can we get enough data about a particular individual to achieve hyper-personalization at mass scale, for anyone and everyone?
A Social Approach to Personalization
Our Klout data science team took a novel approach to this problem. We recognized that some extrapolation is necessary to overcome the data sparsity at the individual level, but we don’t have to extrapolate from other users. We can take advantage of the richness and abundance of public social media data. Because social media is very pervasive, almost everyone uses it to some extent. So existing public social media data is available at a mass scale.
Since public social media data is persistent, and on the whole stays forever (unless the user explicitly deletes posts). This means we can look way back into history to get enough data to infer a user’s interest. Moreover, people’s interests usually don’t change rapidly, so we could recommend content based on the inferred interests of the user. This approach is basically learning from the user’s own past social media interactions.
To draw the analogy with our imaginary friend from the last post: Cortana knows you from seeing how you interact with your friends and families, who you hang out with, what you talk about, what’s your likes/dislikes, etc. From these past social interaction data, Cortana will be able to infer your interest and preference and recommend personalized products for you, even though she has never gone shopping with you.
This approach is more natural, because it is actually how we operate in our physical world. We may not know all the things our friends purchased, or every movie they watched, but from being with them in other social context (e.g. at parties, over dinner, while hiking, etc.), we can definitely learn something about their interest and recommend relevant products and movies they may like.
This approach allows us to use data from the user directly. Although we may not have enough past consumption data from the user, we are not using that data. We are using their own data from a different source (i.e. social media). So we don’t have to extrapolate from other users even though we are still extrapolating. We simply extrapolate from a different context (i.e. the social context) of the same user. This works, because from the psychology of cognitive dissonance, we know that people are generally consistent across different contexts.
This is how our Profile Plus feature offers our community members a unique personalized experience. It works very well as soon as people enable it, because we look back into history. It’s like Cortana has already known you for a long time, even at the beginning. Which means no more cold start. When consumers can immediately recognize the difference, I believe they will embrace it even more.
The Power of Hybrid Approaches
If you are still with me, you are probably wondering, didn’t Amazon solve the personalization challenge with their famous recommender system based on CF? The answer is “yes, they did.” But for me, the more interesting question is how did they overcome the challenge of traditional CF?
Amazon’s famous item-to-item CF is not really a traditional CF. It’s not recommending items based on other users who are similar to you. There is a subtle difference:
it is “people who bought Y also bought X,” which is a recommendation of item X due it’s similar to item Y.
and not “people like you also bought X,” which is a recommendation of item X because it’s the preference of others like you.
So Amazon is merely recommending similar items to what you’ve purchased in the past (i.e. Y, whatever it is).
The genius in Amazon’s item-to-item CF is that it leverages people’s co-ownership on any pair of items (whatever they may be) as an empirical measure of their similarity. So if a lot of people own both a GoPro camera and a Louis Vuitton bag, then these 2 items must be somehow similar, even though there may not be any apparent similarity. In this view, Amazon’s item-to-item CF is actually more comparable to the approach of learning from the user’s own past purchase behavior. However, it is also take advantage of the third approach: learning from other users’ behaviors to determine which items are similar to those you’ve purchased. Therefore, Amazon’s item-to-item CF is really a hybrid method that’s a combination of the 2 approaches.
In machine learning, it is well known that hybrid approaches tend to outperform any single approach, because each can compensate for the systematic errors of the other. There is a whole class of learning algorithms, called ensemble learning, that aims to combine different models to produce the optimal result. Note the famous Netflix Prize was first won by a team that uses ensemble methods. Our social approach that leverages people’s existing social data can also be combined with more traditional collaborative filters. Although we haven’t implemented this hybrid approach yet, it would certainly be an interesting future extension.
Conclusion
With big data, brands can finally see us in multiple dimensions and recognize us as unique. So brands really have no more excuses to not offer their customer a personalized experience. Yet, personalization is a challenging prescriptive analytic method. Amazon has had its early success using an ingenious hybrid approach that combines learning from the user’s past behavior and learning from other users’ collective behaviors.
We took a social approach to mass personalization. This approach involves learning from the user’s own social media interactions. It doesn’t extrapolate from other users, so the recommendations are truly personalized. And because public social media data is both pervasive and persistent, nearly everyone can get personalized content recommendations that should be hyper-relevant from the get-go. This is the social approach we use in Profile Plus to power our own personalized community experience.
BTW, if you are interested to learn more about Profile Plus, please join us for a fireside chat on September 28 at 11AM Pacific Time. Not only will we explore the data science behind Profile Plus in greater depth, we will also look at how it works in and discuss how it can benefit your business. And if you are really interested, we have some real customer examples to share.
Image Credit: Unsplash, geralt, and geralt.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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Hello @petrsvihlik,
Thank you for the question.
By definition Trigger is anything that prompts you to carry out the behavior, with the criteria that
the user must be aware of it
the user must understand what they are prompted to do
So a simple trigger that we are very familiar with it is a phone ring. We can hear it or felt our phone vibrate so we are definitely aware of it. And we know what this trigger is asking us to do (i.e. pick up the phone). So this is valid trigger.
But there are many triggers. Pretty much any changes in the environment that we can sense and understand what they want us to would qualify as a trigger (e.g. alarm, mail notification, a flashing button that want you to click it. etc.).
To learn more about trigger, please see this blog post I wrote about triggers: The Final Touch: Trigger and Gamify.
Hope this helps.
See you again next time.
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Hello @TimWi,
You're welcome. Thx for the comment.
Glad you like this foundation. In the next post we will build on this foundation. You should come back for that.
See you next time, and see you in Oct.
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There is not much time left to cast your vote for SxSW 2017. I’d like to ask for your help again to vote for my sessions. Both of them are related to data science.
Let Your Big Data Drive Your Next Big Win: This workshop shows you how to turn your big data asset into weapons of mass disruption for your business.
Science & Social: The Internet’s New Power Couple: This session shows you how to leverage both social science and data science to reach and convert your audience most effectively.
If you find these topics interesting, please vote for my sessions NOW, because the Panel Picker voting will close Sept 2nd, 2016.
With that, let’s get back to the topic of personalization. As the Charles Dickens novel goes “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness”. This reminds me of the beginning of personalization and the challenges of the early approaches.
Today, we live in a world where access to information is at our fingertip. In this world, relevance instead of raw impression s is more critical to the consumption of content, because an irrelevant impression is often ignored even if it’s right in front of us. Because of our selective attention, personalization becomes very important to the modern day consumers. It aims to create hyper-relevance (i.e. relevance to a single specific person), to minimize the chances of being ignored.
Personalization is all about delivering unique experiences to the consumers based on their individual preferences. It is a very broad concept that includes everything from the personalized email you get from LinkedIn about the career moves in your specific network to something more advanced like the personalized shopping experience in Minority Report. In the digital world, it really comes down to a recommender system, because it can tailor the digital content to a specific user’s interest.
From the discussion in my previous post, recommender systems can be viewed as a prescriptive analytics, because the underlying computation they are performing is an optimization of some objectives (e.g. similarity or relevance to a user’s interest). A recommender system will first score all digital content for their similarity to a particular user’s interest. The top contents that maximize the objective are those that are most similar to the user’s interest, so they will be recommended to the user to give him a unique experience.
There are many different approaches to building a recommender system. Each has its strengths and weaknesses. Today, I’m going to outline some of the early approaches and their challenges. Next time, I will dive deeper to discuss some modern approaches to address these challenges, and we will also examine novel approach we used to power our personalized community experience.
3 Early Approaches to Personalization
There are 3 common approaches to personalization in the industry. First, is the brute force approach. To get the user’s interests and preferences, we simply ask the users. There were many strategies and designs to simplify how a user may self-identify their interests and preferences, even using incentives and rewards. These attempts usually achieve limited success initially with small group of extremely motivated individuals, but fail to engage the bulk of the population. One of the biggest challenges with this approach is that the users must constantly update their interests as they change over time. Very few do this, even among those extremely motivated individuals.
In order to overcome the challenge of people’s changing interests, it was believed that we must take a more adaptive approach and leverage the power of machine learning. So the second common approach to personalization is learning from the user’s own past behaviors.
This is akin to having an imaginary friend, like Cortana (the humanoid AI in Halo), and she always goes out shopping with you. By watching what you browsed through and what you bought enough times, Cortana will be able to learn your interest and preference that way.
This approach is theoretically sound; we should be able to learn about a people’s interest and recommend similar content to what they have consumed in the past. In practice, however, things are always messier. Many learning algorithms require a lot of training data. This means it will take time to collect enough data for the learning algorithm to give an accurate inference about users’ preferences. Consequently, this approach typically does not work well initially (i.e. the cold start problem), but improves as we collect more data from the user. The only problem is that if it doesn’t work well at the beginning, many users could abandon the platform and stop using it altogether. So this approach will only work for the frequent users of the platform (typically a minority), where we can collect sufficient behavior data fast enough. It will not work for infrequent users, because the learning would be too slow.
Many techniques have been developed to speed up learning, so even the infrequent users can get a personalized experience. These techniques all involve some form of generalization or extrapolation to overcome the lack of data for a particular individual. In essence these techniques extrapolate from other people’s data and learn from other users’ collective behaviors.
This is a scenario where Cortana has never gone shopping with you or has not gone with you enough times to learn your interests yet. But if Cortina has gone shopping with many other people similar to you, then she can still infer your interest from other shoppers like you.
The highly popular collaborative filter (CF) leverages what it knows about your preferences (e.g. your book purchases, movie ratings, etc.) to find other users who are potentially similar to you; and then use their behavior (e.g. purchases, ratings etc.) on the new items to estimate your likely preferences. Since these are new items that you have not purchased or rated yet, there is no past preference data. But CFs use other people’s preference data on these new items to estimate your likely preference for these items based on others similarity to you.
Challenges of Traditional Collaborative Filters
Traditional CF does alleviate the cold start problem, but it’s doesn’t solve it. It still requires sufficient amount of preference data (e.g. purchase, ratings, etc.) on new items from other users in order for the CFs to predict your preferences on these items accurately. Moreover, CFs are built on the assumption that people with similar preferences in the past will continue to exhibit that similarity in the future. The validity of this assumption clearly depends on what data we use and how precisely we compute the similarities between individuals.
Learning from other users’ behaviors works well only if we can truly find others who are exactly like you. But we know that people’s interests and tastes are generally pretty unique if we have enough data on the users. The apparent similarity we see between users is an artifact of not having enough data on the user. For example, if we only look at the gender dimension, then it would appear that about half of the world’s population is exactly like me. If we consider other demographic dimensions (e.g. age, education, income, religion, ethnicity, city of residence, occupation, political orientation, etc.), then fewer and fewer people would match me in all those dimensions. In addition, if we further consider behavioral data, social data, and/or other rich sources of big data, then each of us will eventually become unique (i.e. a segment of one).
People only look similar because we don’t have enough data to tell them apart. Today, we have enough big data to uniquely distinguish each individual, so we should be able to give them a personalized experience that’s different from others. The challenges is learning from other users’ behavior will not work very well if everyone is unique, because no two of us are truly alike.
Conclusion
There are 3 common approaches to personalization. Each has its own problems.
Ask the user to self-declare their interest and preference—Problem: few do it, and even fewer will update it.
Learning from the user’s own past behaviors—Problem: data sparsity and cold start could lead to a complete abandonment of the platform for many users.
Learning from other users’ behaviors (e.g. traditional collaborative filtering)—Problem: people’s interests are fairly unique and often don’t generalize across apparently similar individuals.
Keep in mind that people only look similar because brands often do not have enough data to tell them apart. In reality, no two people in the world are exactly alike, but today we have enough big data to see customers in multiple dimensions and distinguish them individually. That is why personalization is so important for brands now.
Next time I will outline how the industry is addressing these challenges. We will also describe our novel approach to this problem.
Image Credit: mat's eye.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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Continuing from Lithium’s thought leadership series, I’ll discuss how brands can find the right gamification model for their business needs, as well as what the future of gamification might look like.
Q: To what degree is gamification customized for different companies in specific industries, and as a result, a different customer base?
A: All gamification has to be designed and customized to the specific behavior you are trying to drive and the specific audience you are trying to gamify. Another common misconception about gamification is that gamification is merely a new technology, so you just have to deploy the points and badges and it will work. This is never the case. Gamification is much more than the technology. It requires sophisticated design and iterations in addition to a deep understanding of human psychology and behavior economics for it to be effective.
If you’re a brick and mortar retail, you probably want to drive consumers to visit your store. But if you’re an e-commerce brand, then you want to drive consumers to visit your website. These are two very different behaviors that require different gamification design. So you should never just take someone’s successful gamification scheme and use it as a cookie cutter for your business.
Q: What questions do companies ask about enlistment, your customers helping you do the work normally done by your employees?
A: It would be “why would a customer help you do work that’s normally done by your employees? Customers are certainly under no obligation to help you do anything, let alone work.”
My answer is “That’s right, customers normally wouldn’t help you (the brand) do anything.” However, we have a powerful tool that can change customers’ behaviors slowly over time. And that tool is gamification. You can gamify the customer to encourage deeper and deeper engagement with the brand, until they co-create with you and become fully enlisted. It’s a process, and it’s not easy, but it can be done if you design the gamification well.
Q: What brands are doing the best jobs of gamification?
A: First, I must put forth a disclaimer: I feel it really doesn’t do justice to just mention my favorite example of gamification because there are too many inspiring examples of gamification in different areas.
With that in mind, my favorite example is Giffgaff’s gamified community business model. It doesn’t just gamify employee collaboration in a department or customer engagement in a community. It’s gamifying their entire business, which involves many parties with wildly different interests (employees, customers, community members, etc.) Moreover, it spans all parts of the business, including marketing, customer service, innovation, etc. To gamify all these moving parts and make them operate so seamlessly that it’s disrupting the incumbent telecom giants is simply amazing.
Q: What gamification trends are you seeing now, and what do you predict for the future?
A: Many gamification tools are being embedded in interactive platforms. The gamification industry started out with many standalone vendors of gamification tools. They offer simple generic gamification tools, such as points, badges, goals, and leaderboards that can be bolted onto systems of interaction. However, gamification often needs significant customization and deep integration with other interaction systems to track, get feedback from, and influence the user behaviors effectively. While there are many success stories, this approach didn’t live up to the promise of gamification. Having learned from this lesson, today’s gamification schemes are often built into interactive systems.
As a data scientist, I don’t like to make predictions lightly, because gamification is still a maturing and rapidly changing field. How it will evolve ultimately depends on what we as a community do with the technology today.
If we use it poorly to drive behavior for purely commercial interest, irrespective of what the consumer gets out of it, then the future of gamification will be pretty grim. Eventually, consumers will realize and recognize these gimmicks that wasted a lot of their time and resources, but didn’t provide value in return. They will resist gamification, much like pop up ads; and then it will be game over for gamification.
On the other hand, if we use gamification in the right way to drive behaviors that have value for the consumer, then gamification’s future will be very bright. It will be infused in anything and everything we do, whether it’s shopping, exercise, or work. It will be so pervasive that I think gamification will no longer exist as a separate discipline. It will just be seen as part of any good design in any product or apps.
I think the evolution of gamification can take any path between these two extremes. But it all depends on what we do today. And I certainly hope that we will use it well and evolve it to the bright side.
Related Blogs
The Era of Personalization: What I Learned from Dr. Wu
Time to be Social with Mass Hyper-Personalization
The Golden Age of Personalization—3 Early Approaches and Their Challenges
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on the Lithium Community, and you can follow him @mich8elwu or Google+.
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Before I begin today, I have a little favor to ask. We just submitted 3 sessions for SxSW 2017, and I need your help to vote for them. Please give us some of your love and support.
First our CMO @KatyKeim would like to show you how to throw a badass customer conference that not only builds deeper relationship with your existing customers, but also attract new ones.
Next, I’d like to show you how to leverage science & social: the internet’s new power couple to reach and convert your audience most effectively.
Lastly, I’d also like to show you how to let your big data drive your next big win in a fun workshop. Although you may not think data science and fun can go together, please give me the opportunity to prove you wrong.
Please vote now, as the panel picker voting will close on Friday, September 2nd.
Alright, let’s get on with data science!
It’s been quite a while since I last wrote about analytics and data science. There is certainly a lot of exciting new developments around data science, especially with deep learning and artificial intelligence (AI). However, data science is actually not a new discipline in business. It was simply disguised under a different name—analytics.
I want to revisit this topic of analytics because it is one of the most nebulous topics in the industry. “Analytics” is one of those catch-all terms that is not very specific. As a result, you can pretty much put any adjective you want in front of “analytics” to tell people more precisely what you really meant. There are literally hundreds and possibly thousands of kinds of analytics just in the business world alone.
Understanding the Countless Varieties of Analytics
To illustrate this points, I will just provide a non-exhaustive list of the kinds of analytics out there. It depends on many aspects of the analytics, such as industry, use case, data type, and even the implementation. For example:
Which industry is the analytics about?
retail analytics
banking analytics
health care analytics
gaming analytics
manufacturing analytics
security analytics
social analytics
What kinds of input data does the analytics processes?
text analytics
image analytics
speech analytics
music analytics
video analytics
Where did the input data came from?
web analytics
community analytics
consumer behavior analytics:
browser analytics
mobile analytics
transactional analytics
What purpose does the analytics serve?
diagnostic analytics
churn analytics
benchmark analytics
performance analytics
engagement analytics
Which business function does the analytics concern?
marketing analytics
multi-channel analytics
sales analytics
service analytics
product usage analytics
people analytics (or HR analytics)
Which vendor (or platform) is the analytics from?
Google analytics
Omniture analytics
Facebook analytics
Twitter analytics
What kind of inference does the analytics perform?
sentiment analytics
intent analytics
fraud analytic
How does the analytics system processes new data?
online analytics
real-time analytics
batch analytics
The list can literally go on for pages with hundreds of esoteric segmentations of analytics. Moreover, we may have any combination of the above categories, because analytics in the retail industry does not preclude it from being real-time. So we can have real-time retail analytics, real-time banking analytics, or even real-time retail sentiment analytics, etc. As you can see, the possible combinations can give rise to an exponential explosion in the types of analytics, and they are all valid.
A Computational Perspective of Analytics
Let’s face it, there are infinite numbers of use cases for big data. So rather than nitpicking about which analytics people are talking about, we can take a more general approach to understand analytics. We can examine the kinds of computations the analytics system is performing under the hood. This perspective simplifies the analytics landscape tremendously, because under this view, there are only 3 classes of analytics. I’ve written about this before, but here is a quick review:
Descriptive Analytics’ purpose is to summarize existing data. The computation being performed here are usually straightforward summary statistics or other derivatives and indices. The precise formula may be very complex, and if you actually write out the formulae it could even be several pages long. However, these are fundamentally the same kind of computations (i.e. an evaluation of some complicated functions).
Predictive Analytics’ purpose is to estimating some unknown quantities of interest. The computation being performed here involves creating and validating a model using existing data. Once the model is fixed, we use it to extrapolate the existing data to regimes where measure data does not exist yet.
Prescriptive Analytics’ purpose is to prescribe actions to guide the decision makers to a desired outcome. The computation being performed here is basically a constrained optimization of some objectives function that is closely linked to the desired outcome.
When I speak on the topic of analytics, people usually have a firm grasp of descriptive analytics as it’s the most common type of analytics in business. Many people have a limited understanding of predictive analytics, thinking that it’s all about forecasting the future. Some recognizes that the most useful forms of predictive analytics are non-temporal. This involves using data you have to estimate data you don’t have, which are not necessarily about the future (examples here). However, few really understand prescriptive analytics. So I will clarify what prescriptive analytics really entails with some examples.
From A/B Testing to Personalization
Because the computation underlying all prescriptive analytics is an optimization, you can think of them as a generalized form of A/B testing (or A/B/n testing, when we are trying to optimize among n possible variants, where n > 2). It tells you which variant (A or B) is better for your desired outcome. The main difference between A/B/n testing and the kinds of optimization in most prescriptive analytics is that n is typically very large (possibly infinite) in the latter.
An illustrative example of prescriptive analytics is a GPS (e.g. Google Map). You tell the GPS where you want to go (i.e. your destination), and it will prescribe a route for you to get there. Keep in mind that there are actually infinite number of routes to get to your destination. So why does your GPS prescribe one specific route? Recall that the computation underlying all prescriptive analytics is an optimization. Since a GPS is an example of prescriptive analytics, what does it optimize?
Most GPS will prescribe a route based on the optimization of the shortest path from your current location to your destination. While there are infinite number of routes to get to your destination, the one that has the shortest distance is usually the one prescribed by your GPS. So the objective that most GPS optimizes is travel distance. Of all the routes that you could take, the one prescribed is the route that minimizes the travel distance. Today, many GPS also allow you to optimize other objectives besides travel distance (e.g. fastest route, fewest turns, or some weighted combination of these). That is why different GPS may prescribe completely different routes for the exact same trip (i.e. same starting point and destination at the same time), because the objective that each GPS optimizes may be different.
Another class of highly commercialized prescriptive analytics is recommender systems (or personalization engines). In today’s information overloaded world, there is more information than we can possibly consume (e.g. web, books, movies, products info, etc.). The personalization engine prescribes a small subset of content to guide the users’ consumption behavior with the desired outcome to satisfy the user’s need. So, what do recommender systems or personalization engines optimize? They are recommending contents that maximizes the similarity or relevance of the content to the user’s preferences. And the action they prescribe at the end is essentially “read these,” “check this out,” etc.
Conclusion
Although there are way too many different kinds of analytics in business, we can simplify the analytics landscape by examining the underlying computation of the different kinds of analytics. Under this computational perspective, there are only 3 kinds of analytics. In short, descriptive analytics tells you what happened in the past; predictive analytics tells you something that you don’t know (whether it’s in the future or something you can’t measure directly), and prescriptive analytics tells you what to do in order to get what you want.
Most people are familiar with descriptive analytics and have a fair understanding of predictive analytics, but few truly understand prescriptive analytics. The key to understanding prescriptive analytics is to ask yourself what objective is being optimized in order to get to a prescribed action that leads to the desired outcome. The optimization of an objective is the common computation underlying all prescriptive analytics.
If you are performing an A/B/n testing, you are optimizing among n variants by brute force trial-and-error. In the GPS example, the objective may be shortest travel distance, shortest travel time (factoring in traffic and speed limit on different routes), fewest turns, or some weighted combinations of these. Finally, recommender systems (or personalization engines) can also be viewed as a class of prescriptive analytics; because they must optimize the similarity or relevance of possible candidate content to a user’s preference in order to prescribe the recommended ones.
I hope these examples give you a deeper understanding of what prescriptive analytics is all about. Are you using any prescriptive analytics that you thought were something else? What is the objective it optimizes? What are the actions it prescribed at the end?
Next time let’s dive deeper and examine some data science challenges in large scale personalization engines.
Related Blogs
Time to be Social with Mass Hyper-Personalization
The Golden Age of Personalization—3 Early Approaches and Their Challenges
An Interview on The Value of Gamification for Today’s Brands and Consumers
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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Hello @ivan73ganchev,
Thank you again for asking your question on my blog. I think this is an excellent question and as you said pretty fundamental question that many could benefit. So let me take a stab at this.
The question about pay vs unlock, is actually not as straight forward as which is better or worse. It depends on a lot of thing such as what are they paying for and what they get when they unlock, the point accumulation rate, what they must do to get those points, etc. But there are clear advantages and disadvantages of each.
If the points dynamics are awarded in the same fashion, you can use the gamification spectrum to get some insights which I will compare and contrast below:
The Pay mechanism (sometimes called the exchange mechanism):
Will deplete your point periodically, so it slows down the user’s progression moving up your gamification ladder. It is harder, so engages fewer people (see how to apply the gamification spectrum in business).
However, for the engaged player who want to play and invest the time to accumulate and re-accumulate points after they spent them, they will play longer. It’s longer lasting.
The Unlock mechanism:
Won’t deplete your points, so it allows users to progress through the gamification spectrum faster and engages a bigger audience ( see a business application of the gamification spectrum ) .
But easier mechanics also implies that that players do not have to invest as much to play, yet this lack of investment also means that it’s easier for the player to drop off faster and people quit earlier.
As suggested in this blog post (and the previous one), to get both ends of the spectrum, you can use both mechanics at different stages of gamification (this is why I suggested that we discuss it here). Early on, you can use the Unlock mechanism to capture a bigger audience, and later on, you can use the Pay mechanism to sustain your gamification over longer periods. To shift from Unlock to Pay mechanism is something that requires you design the transition well.
Some transition strategies you can consider: You mention that this is for access to content. If that is the case, you must have a clear and easy to understand way to distinguish what kinds of content users can unlock and what kinds users must to pay for. The important thing is that the users has to understand it clearly (not you, b/c it will always be obvious to you).
You can also design it that you content is always obtained via unlock, but other privileges (hopefully something bigger and more valuable) are paid for. This requires a deeper understanding of your platform and what users can do on it. Without that it’s hard to comment further.
In general, you should not make people pay for things that they could’ve gotten for free, otherwise you are actually hindering the users. So paying for content is generally not such a good idea (especially at the beginning), unless it's something that is so valuable that people can't say no to it.
In an age of information overload where many people are typically generous and are perfectly willing to answer each other’s questions. Usually the strategy is to give free content to attract people, and when they are there and recognized the value, you can then ask them to pay for something even more valuable. This perception of only paying for something of much greater value than what they can currently get via unlock is a key design element that you must think through carefully. Bottomline, provide value first before you ask the user for anything (see how Pokemon Go did this so well in another one of my recent post).
Anyway, I hope this provide you with some context and framework to think about this problem, which is not so trivial. There is a design component that is very important, even after you've chosen the right strategy and have the shift from Unlock to Pay mechanism down.
Thanks again for asking such excellent question. If you have further question, please feel free to ask me in the comment area of any of my blog posts. I'd be happy to discuss further.
BTW, other experts and practitioners, feel free to chime in if you have thoughts on Pay vs. Unlock.
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Hello @lilim and @Jochen
Apologize for the late reply. Somehow my blog reply notification emails are all routed to my outlook's clutter folder, so I missed a lot of blog replies and I am just slowly getting to these now.
The Power-Law distribution is a fairly universal distribution of that characterizes many natural phenomena. It has relevance to basic science (e.g. physics, chemistry, astronomy, biology, etc.) economics (e.g. income distribution, wealth distribution, GDPs, etc) and distribution of rare events (like earthquakes, etc.), all the way to voluntary participatory behaviors (like social media and gaming motivation).
The fundamental reason that power-law arise is because people are different and participate at different rates. For example I may like to play game for 1 hour/day, but you like to play just slightly longer say 1.5 hours/day. But this difference is accumulated over time to some huge difference. That is if we only play game for a single day, then you would just have 30min (0.5 hours) more play time than me. But what happens if we play games for a year. You would be 182.5 hours ahead of me. The small difference in participation rate due to our individual difference become magnified and accentuated over time. That is the mechanism why the power-law arise.
It is natural, because there is no way to force everyone to behave the same way, we are not robots. And we can't stop time, or slow it down or fast forward it. These are just natural ways that things happen. That is why this power-law distribution not only describe people's level of motivation very well in playing a game. It is also precisely the reason that give rise to the 90:9:1 rule in community participation. I've also written about this subject in a few earlier blog posts.
The 90-9-1 Rule in Reality
The Economics of 90-9-1: The Lorenz Curve
The Economics of 90-9-1: The Gini Coefficient (with Cross Sectional Analyses)
I hope this gives you a deeper understanding of the power-law distribution.
Let me know if you have any questions that you like to discuss further.
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